Research Interest
Extreme-scale implementation of computational materials modeling methods e.g., classical MD, density
functional theory and finite elements to understand multiscale material behavior. Exascale compatible
heterogeneous (CPU+GPU) workflow development to enable data-driven bridging across computational and
physical scales.
- Using Exascale compatible workflows to deploy scale bridging atomistic modeling driven by uncertainty quantification and active learning for mesoscale plasticity in complex solid solutions (e.g., High Entropy Alloys)
- Atomistic coupling of electric field, photo-emission induced irradiation effects and structural defect dynamics driven surface evolution in metals and semiconductors cathodes.
- Deploying charge-equilibration (QEq) + machine-learning interatomic potential (MLP) based models to bridge multiple paradigms of physical and computational length scales.
- Developing and implementing complex free energy landscape search algorithms in combination with variational transition state theory to quantify entropy driven dislocation kinetics.
